FoodForAll

Predict. Rescue. Feed.

When the city's kitchens close, FoodForAll opens. A late-night food platform that serves restaurant surplus to night-shift workers and routes every unsold meal to NGOs and shelters, before it's unsafe.

🌐 Live Demo · 📂 Repository

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📖 About the Project

Food delivery apps stop when restaurants close, but restaurants still have surplus cooked food, and nurses, BPO teams, data-centre staff and security guards still need to eat. Meanwhile, that surplus usually ends up in a landfill.

FoodForAll closes that gap in one night-long loop:

1. Plan: a machine-learning model forecasts each restaurant's leftovers before it closes, using live weather, festivals, public holidays and footfall.
2. Collect: restaurants send surplus to temperature-controlled dark stores, which check it in at a verified safe temperature.
3. Serve: night-shift workers order fresh restaurant food at a fair price, 11 PM – 5 AM.
4. Share: anything unsold 2 hours before its safe time ends, or at the 3:30 AM cut-off, becomes a free donation that one verified NGO can claim, collect and deliver to a shelter.

Nothing reaches a landfill.

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✨ Key Features

For	What they get
🍛 Kitchen managers	A 7-night outlook of expected leftovers with live weather and festivals filled in automatically, plain-English explanations ("Diwali festival over-prep: +46% leftovers"), a closing log and one-click dispatch to a dark store
🏬 Dark-store managers	Live hot-cabinet and fridge temperatures, delivery check-in that turns away unsafe food, a safety countdown for every dish, "Close for the night", and an activity log
🌙 Night-shift workers	A late-night menu with diet filters, popularity and "Only 2 left!" badges, instant reservation at checkout, and live order tracking
🤝 NGO coordinators	An early warning of tonight's expected food before kitchens close, a nearby-hubs map, and claim → pick up → deliver tracking with people-fed logging
📊 Operations lead	Access to every portal, SDG impact analytics, forecast accuracy, and safety drills

Under the hood:

• 🔒 No double-selling, no double-claiming. Stock is reserved atomically: 25 simultaneous buyers for 5 portions means exactly 5 orders succeed. Only one NGO can claim each donation.
• 🌡️ Food safety enforced automatically. Hot food must stay at or above 63 °C (max 6 h) and chilled food at or below 5 °C (max 24 h). Temperature problems shorten a dish's safe time, and serious ones discard it.
• 🌦️ Zero manual input. Weather comes from Open-Meteo (16-day forecast, refreshed hourly). Holidays and festivals come from a generated 2025–2031 Indian and Karnataka calendar.
• 🗣️ Plain language everywhere. No codes or jargon on any page, and each person only sees the pages they use.

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🧭 Try It Yourself

Open the live site and use the Quick sign-in list on the login page. Every account uses the password foodforall2026.

Role	Account	Start here
Night-shift worker	[email protected]	Night Store
Kitchen manager	[email protected]	Restaurant Hub
Dark-store manager	[email protected]	Dark Store
NGO coordinator	[email protected]	NGO Portal
Second NGO (try claiming the same donation)	[email protected]	NGO Portal
Operations lead (all portals)	[email protected]	SDG Impact

⏳ Note: the hosted version runs on serverless infrastructure. The first visit after a quiet spell can take up to a minute while it wakes up, and sample data resets periodically.

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🛠️ Tech Stack

Layer	Technology
Backend	Python, FastAPI, Pydantic v2, SQLAlchemy 2 (SQLite)
Machine learning	scikit-learn (Random Forest), pandas, NumPy
Frontend	Jinja2, Tailwind CSS, Alpine.js, Chart.js, Lucide icons
Security	Argon2 password hashing, JWT authentication, role-based access
Data	Open-Meteo weather API, holidays package (Indian calendar)
Hosting	Vercel

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🧠 How the Forecast Works

• Inputs: weather, evening temperature, day of week, festivals, public holidays, restaurant size and dish category.
• Model: a Random Forest trained on a year of nightly history across 7 restaurants, using real Bengaluru weather.
• Explanations: each prediction comes with a likely range and the reasons behind it ("Rain: +32% leftovers").
• Accuracy: tested on 8 weeks of nights the model never saw. Average error is about 4.4 servings per dish category on ordinary nights and about 4.9 on festival nights, against about 6.5 for a simple average.
• NGO promise: NGOs are promised a safe portion of the forecast, with a reserve held back in case the night is busier.

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🚀 Getting Started

# 1. Clone
git clone https://github.com/Ron102008/FoodForAll.git
cd FoodForAll

# 2. Install (Python 3.10+)
python3 -m venv .venv
.venv/bin/pip install -r requirements-dev.txt

# 3. Run
.venv/bin/uvicorn app.main:app --reload

Open http://localhost:8000. On first start the app creates its database, seeds a year of sample history, fetches live weather and trains the forecast model (about 20 seconds).

# Run the test suite (34 tests, including race-condition tests)
.venv/bin/python -m pytest -q

# Wipe and reseed the sample data
.venv/bin/python seed_data.py --reset

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📁 Project Structure

FoodForAll/
├── app/
│   ├── main.py          # FastAPI app, startup, background routing
│   ├── ml/              # forecasting model, calendar, synthetic history
│   ├── services/        # weather, inventory, NGO routing, food safety, impact
│   └── routers/         # API + page routes for each role
├── templates/           # pages: landing, store, restaurant, dark store, NGO, analytics
├── static/              # JS, CSS, icons, vendored libraries
├── scripts/             # calendar + icon generators
├── seed_data.py         # one year of sample history + tonight's scenario
└── tests/               # pytest suite

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🌍 Impact

FoodForAll tracks food rescued (kg), CO₂e prevented, night-shift orders served, people fed through NGOs, and progress indices for:

• SDG 2: Zero Hunger. The share of donated food that reached people before its safe time ran out.
• SDG 12.3: Halve Food Waste. The share of collected surplus that was eaten rather than wasted.

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⚠️ What's Simulated

• Data: restaurants, accounts, orders and history are sample data generated for demonstration. The forecasting pipeline is real and retrains on actual closing logs.
• Sensors: temperature sensor readings are simulated.
• Weather and calendar: both are real (Open-Meteo and the holidays package).

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🙏 Acknowledgements

• Weather data by Open-Meteo.com
• Holiday calendar generated with python-holidays

Built with 💚 for the night shift